In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study
Background: The advancement of big data analytics calls for careful selection of processing frameworks to optimize machine learning effectiveness. Choosing the appropriate framework can significantly influence the speed and accuracy of data analysis, ultimately leading to more informed decision maki...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Prague University of Economics and Business
2025-08-01
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| Series: | Acta Informatica Pragensia |
| Online Access: | https://aip.vse.cz/artkey/aip-202503-0010_in-memory-versus-disk-based-computing-with-random-forest-for-stock-analysis-a-comparative-study.php |
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